rm useless code

core/general-client/src/pybind_general_model.cpp

@@ -32,7 +32,7 @@ PYBIND11_MODULE(serving_client, m) {
       .def(py::init())
       .def("get_int64_by_name",
            [](PredictorRes &self, int model_idx, std::string &name) {
-             // https://github.com/pybind/pybind11/issues/1042
+             // see more: https://github.com/pybind/pybind11/issues/1042
              std::vector<int64_t> *ptr = new std::vector<int64_t>(
                  std::move(self.get_int64_by_name_with_rv(model_idx, name)));
              auto capsule = py::capsule(ptr, [](void *p) {
@@ -48,10 +48,7 @@ PYBIND11_MODULE(serving_client, m) {
                delete reinterpret_cast<std::vector<float> *>(p);
              });
              return py::array(ptr->size(), ptr->data(), capsule);
-             // return self.get_float_by_name(model_idx, name);
            })
-      //,
-      // py::return_value_policy::reference)
       .def("get_shape",
            [](PredictorRes &self, int model_idx, std::string &name) {
              return self.get_shape(model_idx, name);

python/paddle_serving_client/__init__.py

@@ -329,9 +329,9 @@ class Client(object):
             # result map needs to be a numpy array
             for i, name in enumerate(fetch_names):
                 if self.fetch_names_to_type_[name] == int_type:
+                    # result_map[name] will be py::array(numpy array)
                     result_map[name] = result_batch.get_int64_by_name(mi, name)
                     shape = result_batch.get_shape(mi, name)
-                    # result_map[name] = np.array(result_map[name], dtype='int64')
                     result_map[name].shape = shape
                     if name in self.lod_tensor_set:
                         result_map["{}.lod".format(name)] = np.array(
@@ -339,8 +339,6 @@ class Client(object):
                 elif self.fetch_names_to_type_[name] == float_type:
                     result_map[name] = result_batch.get_float_by_name(mi, name)
                     shape = result_batch.get_shape(mi, name)
-                    # result_map[name] = np.array(
-                    # result_map[name], dtype='float32')
                     result_map[name].shape = shape
                     if name in self.lod_tensor_set:
                         result_map["{}.lod".format(name)] = np.array(